use axum::{
    extract::State,
    http::StatusCode,
    response::Json,
};
use serde::{Deserialize, Serialize};
use serde_json::{json, Value};
use std::sync::Arc;
use crate::AppState;

#[derive(Debug, Deserialize)]
pub struct SuggestConfigRequest {
    pub description: String,
    pub source_type: Option<String>,
    pub destination_type: Option<String>,
}

#[derive(Debug, Serialize)]
pub struct ConfigSuggestion {
    pub confidence: f32,
    pub suggested_config: Value,
    pub explanation: String,
    pub alternatives: Vec<Value>,
}

#[derive(Debug, Deserialize)]
pub struct AnalyzeDataRequest {
    pub topic: String,
    pub sample_data: Vec<Value>,
}

#[derive(Debug, Serialize)]
pub struct DataQualityReport {
    pub overall_score: f32,
    pub issues: Vec<DataIssue>,
    pub recommendations: Vec<String>,
    pub schema_suggestion: Value,
}

#[derive(Debug, Serialize)]
pub struct DataIssue {
    pub severity: String,
    pub field: Option<String>,
    pub description: String,
    pub affected_records: u32,
}

pub async fn suggest_configuration(
    State(_state): State<Arc<AppState>>,
    Json(request): Json<SuggestConfigRequest>,
) -> Result<Json<ConfigSuggestion>, StatusCode> {
    // TODO: 集成AI服务进行配置建议
    tracing::info!("AI配置建议请求: {:?}", request);
    
    let suggestion = ConfigSuggestion {
        confidence: 0.85,
        suggested_config: json!({
            "sync_mode": "incremental",
            "schedule": "0 */5 * * * *",
            "transformations": [
                {
                    "type": "filter",
                    "condition": "record.status == 'active'"
                }
            ]
        }),
        explanation: "基于您的描述，建议使用增量同步模式，每5分钟同步一次，并过滤掉非活跃记录。".to_string(),
        alternatives: vec![
            json!({
                "sync_mode": "full_refresh",
                "schedule": "0 0 2 * * *"
            })
        ],
    };
    
    Ok(Json(suggestion))
}

pub async fn analyze_data_quality(
    State(_state): State<Arc<AppState>>,
    Json(request): Json<AnalyzeDataRequest>,
) -> Result<Json<DataQualityReport>, StatusCode> {
    // TODO: 实现AI数据质量分析
    tracing::info!("AI数据质量分析请求: {:?}", request.topic);
    
    let report = DataQualityReport {
        overall_score: 0.78,
        issues: vec![
            DataIssue {
                severity: "warning".to_string(),
                field: Some("email".to_string()),
                description: "发现15%的邮箱格式不正确".to_string(),
                affected_records: 150,
            },
            DataIssue {
                severity: "info".to_string(),
                field: Some("created_at".to_string()),
                description: "时间戳格式不一致".to_string(),
                affected_records: 50,
            },
        ],
        recommendations: vec![
            "添加邮箱格式验证转换器".to_string(),
            "统一时间戳格式为ISO 8601".to_string(),
            "考虑添加数据去重逻辑".to_string(),
        ],
        schema_suggestion: json!({
            "type": "object",
            "properties": {
                "id": {"type": "string", "format": "uuid"},
                "email": {"type": "string", "format": "email"},
                "created_at": {"type": "string", "format": "date-time"}
            },
            "required": ["id", "email"]
        }),
    };
    
    Ok(Json(report))
}
